Transactional databases typically collect large amounts of time-stamped data relating to an organization's suppliers or customers over time. Examples of such transactional databases include web sites, point-of-sale (POS) systems, call centers, inventory systems, and others. Data mining techniques are often used to derive knowledge from such transactional databases. However, the size of each set of transactional data may be quite large, making it difficult to perform many traditional data mining tasks.
In accordance with the teachings described herein, systems and methods are provided for analyzing transactional data. A similarity analysis program may be used that receives time-series data relating to transactions of an organization and performs a similarity analysis of the time-series data to generate a similarity matrix. A data reduction program may be used that receives the time-series data and performs one or more dimension reduction operations on the time-series data to generate reduced time-series data. A distance analysis program may be used that performs a distance analysis using the similarity matrix and the reduced time-series data to generate a distance matrix. A data analysis program may be used that performs a data analysis operation, such as a data mining operation, using the distance matrix to generate a data mining analysis of the transactional data.